Toward experiment-guided AlphaFold: Researchers overcome AI tool's single-conformation limitation

The AI-based program AlphaFold predicts a protein's 3D structure with remarkable accuracy. However, it tends to reduce heterogeneous structures to a single dominant conformation, or shape, and ...
IEEE

Graph Convolution Based Efficient Re-Ranking for Visual Retrieval

Abstract: Visual retrieval tasks such as image retrieval and person re-identification (Re-ID) aim at effectively and thoroughly searching images with similar content or the same identity. After ...
Nature

Research on traffic flow prediction of progressive graph convolutional networks based on spatio-temporal self-attention mechanism

Traffic flow forecasting is a cornerstone of Intelligent Transportation Systems (ITS), aiming to infer future traffic states from historical observations to support congestion mitigation, route ...
GitHub

CEGANN: Crystal Edge Graph Attention Neural Network

Implementation of Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to perform multiscale classification of materials. Copy CEGAN code in the ...
Nature

Graph convolution network for fraud detection in bitcoin transactions

Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as ...
Semiconductor Engineering

Physics Simulation With Graph Neural Networks Targeting Mobile

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...
Scientific Research Publishing

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting ()

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit ...
GitHub

TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics

the main.py can be run from the command line interface with the following commands, where -f (dataset file) and -r (the number of parid replicates) are the two required parameters. An example command ...
Frontiers

Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction

Urban transportation destination prediction is a crucial issue in the area of intelligent transportation, such as urban traffic planning and traffic congestion control. The spatial structure of the ...
Frontiers

Multi-Dimensional Enhanced Seizure Prediction Framework Based on Graph Convolutional Network

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, ...